_base_ = [
    "../_base_/datasets/coco_detection.py",
    "../_base_/schedules/schedule_1x.py",
    "../_base_/default_runtime.py",
]
model = dict(
    type="YOLOF",
    pretrained="open-mmlab://detectron/resnet50_caffe",
    backbone=dict(
        type="ResNet",
        depth=50,
        num_stages=4,
        out_indices=(3,),
        frozen_stages=1,
        norm_cfg=dict(type="BN", requires_grad=False),
        norm_eval=True,
        style="caffe",
    ),
    neck=dict(
        type="DilatedEncoder",
        in_channels=2048,
        out_channels=512,
        block_mid_channels=128,
        num_residual_blocks=4,
    ),
    bbox_head=dict(
        type="YOLOFHead",
        num_classes=80,
        in_channels=512,
        reg_decoded_bbox=True,
        anchor_generator=dict(
            type="AnchorGenerator", ratios=[1.0], scales=[1, 2, 4, 8, 16], strides=[32]
        ),
        bbox_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0],
            add_ctr_clamp=True,
            ctr_clamp=32,
        ),
        loss_cls=dict(
            type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
        ),
        loss_bbox=dict(type="GIoULoss", loss_weight=1.0),
    ),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(type="UniformAssigner", pos_ignore_thr=0.15, neg_ignore_thr=0.7),
        allowed_border=-1,
        pos_weight=-1,
        debug=False,
    ),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type="nms", iou_threshold=0.6),
        max_per_img=100,
    ),
)
# optimizer
optimizer = dict(
    type="SGD",
    lr=0.12,
    momentum=0.9,
    weight_decay=0.0001,
    paramwise_cfg=dict(
        norm_decay_mult=0.0, custom_keys={"backbone": dict(lr_mult=1.0 / 3)}
    ),
)
lr_config = dict(warmup_iters=1500, warmup_ratio=0.00066667)

# use caffe img_norm
img_norm_cfg = dict(mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="RandomShift", shift_ratio=0.5, max_shift_px=32),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=32),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=8,
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
